Online detection and quantification of epidemics
Online detection and quantification of epidemics
复制标题
DOI：
10.1186/1472-6947-7-29
复制
发表时间：
2007-10-15
影响因子：
3.5
通讯作者：
Valleron, Alain-Jacques
中科院分区：
文献类型：
--
作者：
Pelat, Camille;Boelle, Pierre-Yves;Valleron, Alain-Jacques
背景：时间序列数据在医疗保健中越来越多地可用，特别是为了疾病监测的目的。长期以来，对此类数据的分析一直使用周期性回归模型来检测疫情并估计流行病负担。然而，由于缺乏统计专业知识，该方法的实施可能会很困难。没有专用工具可用于执行和指导分析。结果：我们开发了一个在线计算机应用程序，可以分析流行病学时间序列。该系统可在线获取：http://www.u707.jussieu.fr/periodic_regression/。假设数据由定期基线水平和不规则发生的流行病组成。该程序允许估计定期基线水平和相关的预测上限。后者定义了流行病检测的阈值。流行病的负担被定义为超过基线估计的累积信号。引导用户做出必要的分析选择。我们通过两个例子来说明在线流行病分析工具的用法：对肺炎和流感（P&I）死亡率过高的回顾性检测和量化，以及对胃肠道疾病（腹泻）的前瞻性监测。结论：在线应用程序可以轻松检测流行病学时间序列中的特殊事件，并对超额死亡率/发病率相对于基线的变化进行量化。对于现场和公共卫生从业人员来说，它应该是一个有价值的工具。
Background: Time series data are increasingly available in health care, especially for the purpose of disease surveillance. The analysis of such data has long used periodic regression models to detect outbreaks and estimate epidemic burdens. However, implementation of the method may be difficult due to lack of statistical expertise. No dedicated tool is available to perform and guide analyses. Results: We developed an online computer application allowing analysis of epidemiologic time series. The system is available online at http://www.u707.jussieu.fr/periodic_regression/. The data is assumed to consist of a periodic baseline level and irregularly occurring epidemics. The program allows estimating the periodic baseline level and associated upper forecast limit. The latter defines a threshold for epidemic detection. The burden of an epidemic is defined as the cumulated signal in excess of the baseline estimate. The user is guided through the necessary choices for analysis. We illustrate the usage of the online epidemic analysis tool with two examples: the retrospective detection and quantification of excess pneumonia and influenza (P&I) mortality, and the prospective surveillance of gastrointestinal disease (diarrhoea). Conclusion: The online application allows easy detection of special events in an epidemiologic time series and quantification of excess mortality/morbidity as a change from baseline. It should be a valuable tool for field and public health practitioners.